Reasoning and Learning Methods for Diagnosis in Oriental Medicine

한의 진단 추론과 진단 학습 방법

  • Kim, Sang-Kyun (Information Research Center, Korea Institute of Oriental Medicine) ;
  • Kim, Jin-Hyun (Information Research Center, Korea Institute of Oriental Medicine) ;
  • Jang, Hyun-Chul (Information Research Center, Korea Institute of Oriental Medicine) ;
  • Kim, An-Na (Information Research Center, Korea Institute of Oriental Medicine) ;
  • Yea, Sang-Jun (Information Research Center, Korea Institute of Oriental Medicine) ;
  • Kim, Chul (Information Research Center, Korea Institute of Oriental Medicine) ;
  • Song, Mi-Young (Information Research Center, Korea Institute of Oriental Medicine)
  • 김상균 (한국한의학연구원 정보연구센터) ;
  • 김진현 (한국한의학연구원 정보연구센터) ;
  • 장현철 (한국한의학연구원 정보연구센터) ;
  • 김안나 (한국한의학연구원 정보연구센터) ;
  • 예상준 (한국한의학연구원 정보연구센터) ;
  • 김철 (한국한의학연구원 정보연구센터) ;
  • 송미영 (한국한의학연구원 정보연구센터)
  • Published : 2009.10.25

Abstract

We in this paper propose the method for diagnosis patients through the reasoning based on the diagnosis ontology in oriental medicine. In prior studies, it is simply diagnosed with the information of main symptoms, optional symptoms, and tongue / pulse. In addition, ontology itself has subjective opinions of oriental medical doctors for patients in form of axioms. There is a problem in latter case that it is difficult for other oriental medical doctors to change knowledge within the ontology. In order to solve these problems, we have constructed the diagnosis ontology and the reasoning algorithm as followings: First, in order to raise the diagnosis accuracy, we constructed the diagnosis ontology with pattern identifications, main symptoms, optional symptoms, and tongue / pulse. We also utilize the diagnosis points described in the pathology textbook, which has been studied in all of domestic oriental medical colleges. This information is represented as OWL instances in ontology, not OWL axioms so that it can be easily updated. Second, we suggest the algorithms for diagnosis reasoning and learning method based on the ontology. We have implemented the reasoning and learning system according to the diagnosis algorithm. In future study, we will construct the diagnosis ontology with all of pattern identifications and symptoms within the pathology textbook.

Keywords

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